The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crosso...
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doaj-49c49288fed440958fd8afec1b3079c72021-08-26T14:02:11ZengMDPI AGMathematics2227-73902021-08-0191909190910.3390/math9161909The Real-Life Application of Differential Evolution with a Distance-Based Mutation-SelectionPetr Bujok0Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech RepublicThis paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.https://www.mdpi.com/2227-7390/9/16/1909differential evolutiondistance-basedmutation-selectionreal applicationexperimental studyglobal optimisation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Petr Bujok |
spellingShingle |
Petr Bujok The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection Mathematics differential evolution distance-based mutation-selection real application experimental study global optimisation |
author_facet |
Petr Bujok |
author_sort |
Petr Bujok |
title |
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection |
title_short |
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection |
title_full |
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection |
title_fullStr |
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection |
title_full_unstemmed |
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection |
title_sort |
real-life application of differential evolution with a distance-based mutation-selection |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2021-08-01 |
description |
This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly. |
topic |
differential evolution distance-based mutation-selection real application experimental study global optimisation |
url |
https://www.mdpi.com/2227-7390/9/16/1909 |
work_keys_str_mv |
AT petrbujok thereallifeapplicationofdifferentialevolutionwithadistancebasedmutationselection AT petrbujok reallifeapplicationofdifferentialevolutionwithadistancebasedmutationselection |
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1721191809076428800 |